The adoption of AI is on the rise, and its use in cybersecurity shows both its advantages and disadvantages. AI can improve threat detection, but it also introduces new attack vectors.
The 2024 McKinsey Report shows that the market for cybersecurity tools will grow by 12.4% each year until 2027, surpassing historic growth, with over 70% of large organizations across the industry planning to invest in AI-enabled security tools. However, AI can also increase risk. Since the launch of the widely available generation AI platform in 2022, phishing attacks have increased by 1,265%, the report found.
Comparing the advantages and disadvantages of AI in cybersecurity can be challenging for many organizations. However, by implementing best practices such as human surveillance and clear security policies, teams can reduce risk and use AI in their cybersecurity operations with confidence.
Five Benefits of AI in Cybersecurity
AI has fundamentally changed the way cybersecurity practitioners detect and respond to new threats. AI helps in cybersecurity areas such as threat detection, response times, predictive analytics, alerts, and patch management.
1. Improved real-time threat detection
AI security tools can dramatically improve threat detection by analyzing large amounts of network traffic from a variety of sources, including user behavior and system logs. For example, AI tools can identify unusual patterns of network traffic. This could indicate a sophisticated persistent threat that traditional security tools may overlook.
AI tools are also excellent at detecting and classifying patterns. Machine learning models trained on large datasets can efficiently identify and classify malicious patterns based on historical data. This helps identify threats and enhance network security.
2. Improved response time
Automating incident investigations streamlines the detection, analysis and containment of security threats.
When an incident occurs, AI systems can immediately correlate real-time data across a variety of security platforms, including firewalls, endpoint protection systems, intrusion prevention and intrusion detection systems (IDSES), and security information and event management tools. These integrations allow AI to quickly rebuild the attack timeline.
For example, in ransomware attacks, AI tools can identify root causes and affected systems, prioritize risks, and recommend containment strategies. However, with AI, the process can start within minutes, significantly minimizing potential damage.
3. Predictive Security Analysis
By analyzing historical security incidents, AI models can predict potential vulnerabilities before threat actors exploit them. For example, financial institution AI tools can recognize anomalous authentication patterns based on similar patterns detected in previous violations.
4. Reduced false positive alerts
AI can combat alert fatigue by reducing false positive notifications. For example, large financial institutions handle many daily transactions. Traditional security tools can flag large numbers of login attempts as suspicious, and can trigger hundreds of daily alerts for human analysts to manually inspect.
On the other hand, AI-driven systems can analyze historical transaction data, user behavior, and other contextual information to identify normal operational patterns. This allows security teams to focus on real threats rather than spending time investigating benign anomalies.
5. Automatic patch management
AI automation features can assist with everyday security tasks.
For example, AI can automate vulnerability assessment across thousands of systems in an IT environment. This improves the ability of security teams to make informed decisions, such as adjusting access controls based on risk scores and continually monitoring security configurations for gaps. Once a critical vulnerability is discovered, AI systems can identify affected assets, prioritize patches, and verify successful repairs.
Five disadvantages of AI in cybersecurity
AI security tools offer important advantages in cybersecurity, but they also have some drawbacks.
1. High implementation cost
Using AI in cybersecurity requires initial investment, especially for organizations with both on-premises and cloud IT environments. Deploying AI across hybrid environments may require specialized hardware such as GPUs for skilled personnel such as model training, enterprise-grade software licensing, and data scientists and security architects.
2. The complexity of maintenance
AI security tools require continuous model improvements, regular data updates, and continuous performance optimization. Organizations should maintain dedicated teams for model retraining, functional engineering, and addressing issues. Smaller organizations often struggle to meet these requirements when they lack technical expertise and budgets to effectively maintain sophisticated AI security tools.
3. Hostile attacks
AI tools face unique attacks targeting the underlying machine learning infrastructure. Common attack vectors include:
- Data addiction attack. In data addiction attacks, adversaries manipulate training data by injecting carefully crafted malicious samples to force AI tools to misclassify future attacks.
- Extraction attack. The enemy shows the parameters and decision boundaries of the inverse engineer model to reveal model architecture and training data. The data revealed may contain your own source code or confidential information.
- Evasion attack. Enemies exploit blind spots in their models, such as changing attack patterns and slightly changing the malware signature to prevent the system from detecting certain types of malware while still maintaining system-wide functionality.
4. Privacy impact
Some AI security tools process huge amounts of sensitive data. For example, healthcare organizations that implement AI-based threat detection should ensure HIPAA compliance when processing patients and other sensitive medical records. Similarly, financial institutions must maintain PCI DSS standards while analyzing customer transaction patterns.
5. Ethical Considerations
Cybersecurity AI can cause complex ethical issues. For example, behavior-based anomaly detection could incorrectly flag a particular individual or group. Furthermore, it is accountability to determine when AI tools will incorrectly make security decisions.
6 Best Practices for Using AI in Cyber Security
Cybersecurity tools powered by AI can help organizations improve security and mitigate threats. Use these best practices to manage your risk.
1. Check human surveillance
Maintain human surveillance on important decisions. AI can detect and flag abnormal behavior, but some decisions, such as blocking access to sensitive resources or escalating incidents, should always involve human reviews to prevent false positives and ethical issues.
2. Establish a clear AI security policy
AI security policies ensure fair use of AI, define boundaries of accountability, and address challenges associated with automated decision-making. Organizations need to clearly understand the risks and limitations of AI operating without human intervention. Organizations operating in highly regulated industries such as healthcare, for example, must specify where AI systems can work, such as when processing sensitive patient data.
3. Use a high quality training data set
Train models with fair and high quality data sets to minimize inaccuracy. To enhance accuracy and protect user privacy, tailor training data to suit the specific features of the AI tool. For example, models designed to detect rogue login attempts while avoiding rogue customer information should train on logs of failed attempts.
4. Combine AI with traditional security tools
Combine AI tools with traditional security tools such as firewalls, intrusion detection systems, and manual auditing to build a defense-in-depth strategy. This approach provides comprehensive protection against an array of cyber threats.
For example, retailers can integrate AI-driven endpoint protection systems with existing identity. AI systems identify advanced malware based on their behavior, while IDS focuses on signature-based detection of known threats. This layered approach protects against both new attack techniques and previously implemented attack techniques.
5. Keep your model up to date
Regularly update AI tools and underlying models to maintain effectiveness against evolving threats. Continuous updates allow the system to detect and respond to the latest attack vectors. For example, if your organization uses AI-powered phishing detectors, the tools should be updated regularly with the latest attack technologies to prevent emerging phishing attacks.
6. Implement access control
Secure AI system access with strong authentication mechanisms, such as multifactor authentication (MFA). This allows only authorized personnel to change AI configurations and access sensitive data, reducing the risk of insider threats and unauthorized changes.
For example, tech companies deploying AI-powered threat analysis tools may use MFA to protect access to system dashboards. Only people with MFA access can change configurations and check confidential analyses. This prevents unauthorized users from tampering with important settings for malicious purposes.
Nihad A. Hassan is an independent cybersecurity consultant, digital forensics expert, cyber open source intelligence, blogger and author. For over 15 years, Hassan has been actively researching various areas of information security and has developed numerous cybersecurity education courses and technical guides.
